Journal of Ecology 2011, 99, 1460–1469
doi: 10.1111/j.1365-2745.2011.01875.x
Identifying population- and community-level
mechanisms of diversity–stability relationships in
experimental grasslands
Christiane Roscher1*, Alexandra Weigelt2, Raphael Proulx3†, Elisabeth Marquard4,
Jens Schumacher5, Wolfgang W. Weisser6 and Bernhard Schmid7
1
UFZ, Helmholtz Centre for Environmental Research, Department of Community Ecology, Theodor-Lieser-Strasse 4,
06120 Halle, Germany; 2Institute of Biology I, University of Leipzig, Johannisallee 21-23, 04103 Leipzig, Germany;
3
Max Planck Institute for Biogeochemistry, POB 100164, 07701 Jena, Germany; 4UFZ, Helmholtz Centre for Environmental Research, Department of Conservation Biology, Permoserstrasse 15, 04318 Leipzig, Germany; 5Institute of
Stochastics, University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany; 6Institute of Ecology, University of Jena,
Dornburger Strasse 159, 07743 Jena, Germany; and 7Institute of Evolutionary Biology and Environmental Studies,
University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
Summary
1. While positive effects of biodiversity on temporal stability of communities have been demonstrated in theoretical and empirical studies, diversity–stability relationships at the population level
remain poorly understood.
2. We investigated temporal variability of plant populations in experimental grassland plots of
varying species richness (1, 2, 4, 8, 16–60 species), functional group richness and composition (presence ⁄ absence of legumes · grasses · small herbs · tall herbs) in a long-term biodiversity experiment from 2003 to 2009 (‘Jena Experiment’).
3. Average population stability, defined as the reciprocal of the coefficient of variation of aboveground biomass production over time, differed largely between species but was generally higher in
grasses and small herbs than in legumes and tall herbs. Furthermore, population stability was positively related to a species’ proportional contribution to community biomass. Thus, an increasing
number of subordinate species explained lower average population stabilities at higher diversity
levels.
4. A negative covariance (CV) across all species-richness levels suggested negatively correlated species dynamics. Species belonging to different functional groups fluctuated asynchronously, while
species dynamics within functional groups were more synchronous. Community-wide species synchrony decreased with increasing species richness, and temporal stability at the community level
increased.
5. Synthesis: Our results suggest that diversity–stability relationships are driven by fluctuations in
the population biomass of individual species which are less synchronized in more diverse than in less
diverse mixtures and monocultures. Dominant plant species tend to be more stabilized than subdominant species, independently of community species richness. However, asynchrony of population
dynamics outweighs decreasing population stability with increasing species richness, resulting in
higher temporal stability at the plant community level.
Key-words: biodiversity, covariance, plant population and community dynamics, population
stability, synchrony, temporal stability
Introduction
*Correspondence author. E-mail: [email protected]
†Present address: Chaire UQTR en Biologie Systémique de la Conservation, Université du Québec à Trois-Rivières, 3351 Des Forges,
Trois-Rivières, QC, G9A 5H7, Canada.
The consequences of biodiversity loss for ecosystem functioning have gained increasing attention in current ecological
research (Chapin et al. 2000; Hooper et al. 2005). One
fundamental, but controversially discussed characteristic of
2011 The Authors. Journal of Ecology 2011 British Ecological Society
Diversity–stability relationships 1461
biodiversity is its role in promoting the temporal stability of
ecosystems at different levels of organization, i.e. community,
functional-group and population level (McCann 2000; Bai
et al. 2004; Ives & Carpenter 2007; Proulx et al. 2010).
Biodiversity, defined as the number of different species in a
given area, may have contrasting effects on community or population variability (King & Pimm 1983; Tilman 1996; Jiang &
Pu 2009). In general, species diversity has a positive effect on
the temporal stability of aggregate community properties such
as total biomass production (Cottingham, Brown & Lennon
2001; Loreau et al. 2002). In contrast, the effect of increasing
species diversity on the stability of populations, which has
mostly been studied for plants, is more variable (Campbell,
Murphy & Romanuk 2011). So far, stabilizing (Kolasa &
Li 2003; Valone & Hoffmann 2003a,b) and neutral effects
(McGrady-Steed & Morin 2000; Steiner et al. 2005; Flynn
et al. 2008) on populations have been described, but most studies in grassland systems have reported a destabilizing effect of
species diversity on temporal variation in population aboveground biomass production (Tilman 1996; Lehman & Tilman
2000; Caldeira et al. 2005; Tilman, Reich & Knops 2006; van
Ruijven & Berendse 2007; Hector et al. 2010). Several studies
have found that dominant species have greater stability than
subordinate species (Lepš 2004; Steiner et al. 2005; Polley,
Wilsey & Derner 2007; Grman et al. 2010). Therefore, species
abundance distributions are likely to play an important role in
plant diversity–population stability relationships, which
implies that diversity effects on temporal stability may be
dampened by the dynamics of dominant species (Sasaki &
Lauenroth 2011). However, controversial results on relationships between species diversity and population dynamics have
also been attributed to confounding effects owing to differences in the study systems ranging from laboratory microcosms to natural ecosystems, the level of trophic complexity,
the experimental design and the method of calculating population variability (Romanuk, Vogt & Kolasa 2009; Campbell,
Murphy & Romanuk 2011).
Both statistical and biological mechanisms, which are often
not independent of each other, may induce a positive relationship between species diversity and temporal stability
Cottingham, Brown & Lennon 2001). Stabilization at the community level may occur by statistically averaging the temporal
dynamics of uncorrelated populations. This has been called
the ‘portfolio effect’ (Doak et al. 1998; Tilman, Lehman & Bristow 1998), where independent species fluctuations average
out with increasing species richness, resulting in a lower variability of an aggregate community property. Statistical
averaging occurs when temporal species fluctuations (=variance) increase more slowly with species abundance (=mean)
than predicted by the mean-to-variance power law.
Increasing species richness may also increase the chance for
asynchronous fluctuations of populations, leading to lower
covariances among species within a community (Tilman 1999;
Lehman & Tilman 2000; Loreau & de Mazancourt 2008). Population dynamics are driven by three main forces: intra- and
interspecific density dependence, environmental fluctuations
and demographic stochasticity (Loreau & de Mazancourt
2008). Biological mechanisms that can lower covariances
among species are competitive interactions, physiologically or
life history determined species-specific responses to environmental changes, or stochastic processes (Ives, Klug & Gross
2000). When species respond similarly to environmental fluctuations, positive covariances among species may increase community variability, while differential responses of species to
environmental fluctuations may lower covariances among species and decrease community variability. Functionally similar
species that respond similarly to environmental fluctuations
are also likely to compete for shared resources, i.e. covariances
are probably not independent of interaction strengths (Hughes
& Roughgarden 2000). Compensatory effects among species,
leading to a reduction in temporal variability at community
level, and an increase in the temporal mean of the corresponding aggregate community property are the fundamentals of the
‘insurance hypothesis’ (Yachi & Loreau 1999).
While empirical studies have repeatedly shown that species
richness increases the temporal stability of aggregate community properties, fewer studies exist that tested theoretical predictions about the effects of species richness on temporal
stability at the population level and on compensatory dynamics among species or functional groups (e.g. van Ruijven &
Berendse 2007; Isbell, Polley & Wilsey 2009). The present
study is based on data recorded over a study period of 7 years
(2003–2009) in a large grassland biodiversity experiment (‘Jena
Experiment’; Roscher et al. 2004) comprising 82 plant communities varying in species richness from 1 to 60 and in functional
group richness from 1 to 4 (functional groups: legumes,
grasses, small herbs and tall herbs). We tested the hypotheses
that (i) temporal stability at the population level decreases with
increasing species richness, (ii) temporal stability at the population level increases with a species’ proportional contribution to
community biomass, (iii) population dynamics of different species are less synchronized between than within plant functional
groups and (iv) population dynamics of different species are
less synchronized in species-rich than in species-poor communities.
Materials and methods
EXPERIMENTAL DESIGN
This study was carried out in a large biodiversity experiment, the
‘Jena Experiment’ (Roscher et al. 2004), established in May 2002 on
a former agricultural field. The study site is located in the floodplain of
the River Saale nearby Jena (Thuringia, Germany, 5055¢ N,
1135¢ E, 130 m a.s.l.). There, mean annual air temperature is
9.3 C, and average annual precipitation is 587 mm (Kluge & MüllerWestermeier 2000). The soil is a Eutric Fluvisol developed from up to
2-m-thick fluvial sediments that are almost free of stones. Soil texture
changes gradually from sandy loam in the vicinity of the river to silty
clay with increasing distance to the river.
Plant communities were established from a pool of 60 plant species
common to Central European semi-natural grasslands (Arrhenatherion communities, Ellenberg 1988). Species were classified into four
functional groups according to results of a cluster analysis of a literature-based trait matrix describing morphological, phenological and
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469
1462 C. Roscher et al.
physiological characteristics: grasses (16 species), legumes (12 species), tall herbs (20 species) and small herbs (12 species). All possible
combinations of species number (1, 2, 4, 8 or 16 species) and plant
functional group number (1–4 functional groups, presence ⁄ absence
of legumes · grasses · small herbs · tall herbs) were realized,
resulting in a near-orthogonal design of the experiment. Each
species-richness level had 16 replicates with the exception of the
16-species mixtures because not enough legumes and small herbs were
in the species pool to assemble them in mixtures with 16 species of the
same functional group. Mixture compositions were determined by
random drawing with replacement. In addition, four replicates of the
60-species mixture were established. In total, 82 plots of 20 · 20 m
size were sown. The field site was divided into four experimental
blocks parallel to the river each containing an equal number of plots
per species-richness level. Sown seed density amounted to 1000 germinable seeds per m2. In mixtures, all species were sown with equal
proportions (for further details, see the study of Roscher et al. 2004).
Experimental plots were mown twice a year (early June and
September), and the plant material was removed, as it is typical for
hay meadows. Two yearly weeding campaigns (early April and July)
served to maintain the target species composition. Herbicides were
used as spot treatments against selected weed species (Cirsium arvense
(L.) Scop., Rumex sp.), and where sown species combinations allowed
their application (against herbs in pure grass communities and
against grasses in pure herb communities, respectively). Mowing,
weeding and herbicide spraying were performed blockwise in rotating
order. Plots were not fertilized during the experimental period.
DATA COLLECTION
Our analyses are based on data collected at estimated peak biomass
before first mowing in late May from 2003 to 2009. Above-ground
plant biomass was harvested by clipping the vegetation 3 cm above
ground on rectangles of 20 · 50 cm size. Four samples (only three in
May 2005, 2008 and 2009) were taken per plot. The location of sample frames was allocated at random for each harvest. Samples were
sorted to sown species (species sown at a particular plot), total weeds
(species not sown at a particular plot) and detached dead plant material; samples were then dried to constant weight (70 C, 48 h).
Above-ground species biomass per harvest was calculated as the
mean of the four (or three) samples per plot. For further details and
data, see the study of Weigelt et al. (2010).
MEASURES OF TEMPORAL VARIABILITY
Temporal stability (S) was quantified as the reciprocal of the coefficient of variation (Tilman 1999) using the ratio of the mean (l) to the
standard deviation (r):
S ¼ l=r:
eqn 1
Temporal stability was calculated firstly for the time series of biomass data for each species in each experimental community (=population stability SPop) and secondly for the time series of community
biomass, i.e. sum of species biomass (=community stability SCom).
Temporal variances (r2) and means (l) in species biomass were
analysed according to a power–function relationship between the two
parameters (Taylor 1961),
r2 ¼ clz ;
logarithm of r2 and the logarithm of l [log(r2) = c + z * log(l)]
and describes diversity effects on the strength of the portfolio effect.
Based on the assumption that community biomass varies independently of species richness and is equally distributed among species
and that covariances are absent, the variance in community biomass
is simply related to the sum of individual species variances (Doak
et al. 1998). Under these assumptions, the summed variances
decrease (and community stability increases) with diversity when
z > 1. At the same time, population stability decreases with diversity
for z < 2 and increases when z > 2 (Tilman 1999; Cottingham,
Brown & Lennon 2001). The z values were fitted for each species separately across all communities in which the species occurred and across
species combining all communities. Reduced major axis regression
(RMA) as implemented in the lmodel2 package of the statistical software R (R Development Core Team, http://www.r-project.org) was
applied, and significance of slopes was tested by 499 random permutations.
Summed variances, summed covariances, variance CV, covariance
CV and variance ratio VR were calculated across all species per plot,
at the between-functional-group level (summing biomass per functional group) and at the within-functional-group level, where, for a
given plot, Pi is biomass of species population i (or functional group,
respectively), and
Variance CV ¼ ½RvarðPi Þ=½Rl2
eqn 3
Covariance CV ¼ ½RconðPi ; Pj Þ=½Rl2
eqn 4
(Loreau & de Mazancourt 2008). The variance ratio (VR) relates
the variance of an aggregated variable (C = sum of the component
populations) to the variance of individual species populations Pi,
where
varðCÞ
VR ¼ Pn
i¼1 varðPi Þ
eqn 5
and
varðCÞ ¼ ½
n
X
varðPi Þ þ ½2
i¼1
n X
i1
X
covðPi Pj Þ:
eqn 6
i¼1 j¼1
When species vary independently, their covariance is zero, and
summed species variances equal the variance of the community property. When species do not vary independently, summed covariances,
being predominantly negative or positive, cause a decrease or increase
in overall variability. A VR < 1 indicates that the summed covariances are negative, suggesting negatively correlated population
dynamics of species, while VR > 1 occurs when the sum of covariances among species is positive, suggesting positively correlated population dynamics of species. A VR = 1 indicates that positive and
negative covariances among species cancel each other out (Gonzalez
& Loreau 2009).
In addition, community-wide synchrony was calculated following
Loreau & de Mazancourt (2008), as follows:
varðCÞ
uP ¼ Pn pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 :
ð i¼1 varðPi ÞÞ
eqn 7
This statistic ranges between 0, indicating complete asynchrony,
and 1, indicating perfect synchrony among species.
eqn 2
where c is a constant and z is a scaling coefficient. The logarithmic
transformation of this equation results in a linear mean–variance
function, where z is the slope of the positive relationship between the
STATISTICAL ANALYSES
Data were analysed with the statistical software R2.11.1 (R Development Core Team, http://www.r-project.org). Analyses of population
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469
Diversity–stability relationships 1463
stability SPop across species were performed with linear mixed-effects
models using the lme function in the nlme package (Pinheiro & Bates
2000) of the statistical software R. Block and plot identity were treated as random factors in a nested sequence to account for differences
between the experimental blocks and the statistical dependency
among species occurring in the same plot. Starting from a constant
null model, the fixed-effects species richness (as log-linear term) and
number of functional groups (as linear term) were entered, followed
by a term for species identity and its interaction with species richness
and number of functional groups. In alternative models, species identity was replaced by terms for functional group identity (=factor with
four levels) or contrasts for the identity of particular functional
groups. Finally, species proportional contribution to community biomass based on biomass per species in each experimental community
as a mean value over time was entered as a covariate before the experimental factors to explore the relationship between SPop and species
proportions in community biomass. We used a species’ proportional
contribution to community biomass to account for the effect of
species richness on biomass partitioning (i.e. more plant species in a
community may imply proportionally less biomass per species)
without explicitly eliminating the effect of species richness on population biomass (i.e. if community biomass increases with species richness, more plant species does not necessarily imply less biomass per
species). Then, all species occurrences were grouped according to
their contribution to mixture biomass as dominants (>25%), intermediates (5–25%) and subordinates (<5%). Firstly, a grouping term
was entered instead of species identity in modelling, and secondly,
each group of data was analysed separately with the model described
above. The maximum-likelihood method and likelihood ratio tests
(L ratio) were applied to assess the statistical significance of model
improvement by adding the fixed effects.
Variables at the plot level were analysed with analysis of variance
(anova) with sequential sums of squares (type I SS). Following the a
priori hypotheses of the ‘Jena Experiment’, model terms were entered
in the following sequence: block, species richness (as log-linear term)
and number of functional groups (as linear term). In a series of analyses, we fitted alternatively the presence ⁄ absence of each plant
functional group after the term for number of functional groups. In
analyses of summed variances and covariances, variance CV, covariance CV and VR at the between- or within-functional-group level,
only communities with two and more functional groups or species per
functional group, respectively, were included. This was done to avoid
a bias through constant levels of covariance CV (=0) and VR (=1)
in communities containing one functional group or one species only.
Alternatively, the species-richness term was separated into a monoculture vs. mixture contrast and a log-linear contrast within mixtures,
where constant values per definition are restricted to monocultures
(covariance CV, VR, community-wide species synchrony uP across
all species).
Results
POPULATION STABILITY (HYPOTHESES 1 AND 2)
Population stabilities (SPop; l ⁄ r) based on peak above-ground
biomass varied strongly among species (Fig. 1). Population
stability on average decreased with increasing species richness,
which was significant for 30 of 60 species in separate per-species analyses (Table 1; Fig. 2). The number of functional
groups fitted after species richness did not explain additional
variation in population stability (Table 1). However, population stability within plots and its variation in response to
increasing species richness and number of functional groups
differed largely between species (significant interaction terms
for ‘Species ID · SR’ and ‘Species ID · FG’). Population stability varied among species assigned to different plant
functional groups with average values of 0.99 (±0.05 SE) for
grasses, 0.96 (±0.04 SE) for small herbs, 0.83 (±0.03 SE) for
legumes and 0.81 (±0.03 SE) for tall herbs. However, the relationship between population stability and increasing species or
functional-group richness did not vary among the four
functional groups (see Table 1 for nonsignificant interaction
terms among ‘SR’, ‘FG’ and ‘Functional group ID’, or contrasts for any particular functional group).
Grasses
Legumes
Tall herbs
Small herbs
2.0
1.5
1.0
0.5
0.0
Trisetum flavescens
Taraxacum officinale
Dactylis glomerata
Alopecurus pratensis
Knautia arvensis
Arrhenatherum elatius
Onobrychis viciifolia
Festuca rubra
Galium mollugo
Glechoma hederacea
Veronica chamaedrys
Plantago lanceolata
Medicago x varia
Leucanthemum vulgare
Lotus corniculatus
Rumex acetosa
Plantago media
Achillea millefolium
Centaurea jacea
Poa pratensis
Lathyrus pratensis
Ranunculus acris
Vicia cracca
Poa trivialis
Avenula pubescens
Leontodon hispidus
Crepis biennis
Trifolium pratense
Festuca pratensis
Phleum pratense
Bellis perennis
Geranium pratense
Trifolium repens
Sanguisorba officinalis
Leontodon autumnalis
Prunella vulgaris
Pimpinella major
Bromus erectus
Carum carvi
Holcus lanatus
Anthoxanthum odoratum
Bromus hordeaceus
Ranunculus repens
Ajuga reptans
Cirsium oleraceum
Primula veris
Heracleum sphondylium
Tragopogon pratensis
Trifolium hybridum
Luzula campestris
Cynosurus cristatus
Medicago lupulina
Daucus carota
Campanula patula
Trifolium campestre
Trifolium fragiferum
Anthriscus sylvestris
Trifolium dubium
Pastinaca sativa
Cardamine pratensis
Population stability SPop
2.5
Fig. 1. Population stability SPop (l ⁄ r) based on peak above-ground biomass of species from 2003 to 2009. Values are means (±1 SE) across all
plots where a particular species occurred.
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469
1464 C. Roscher et al.
Table 1. Summary of mixed-effects model analysis for population
stability SPop (=l ⁄ r) based on peak above-ground biomass of
species from 2003 to 2009
Species richness (SR)
Functional group number (FG)
Species ID
Functional group ID
Legumes
Grasses
Small herbs
Tall herbs
Species ID · SR
Functional group ID · SR
Legumes · SR
Grasses · SR
Small herbs · SR
Tall herbs · SR
Species ID · FG
Functional group ID · FG
Legumes · FG
Grasses · FG
Small herbs · FG
Tall herbs · FG
L ratio
P
49.19
0.23
574.16
23.18
6.65
11.11
4.53
8.69
85.37
0.22
0.17
0.12
<0.01
<0.01
91.90
6.53
2.15
3.81
0.76
2.37
<0.001fl
0.632
<0.001
<0.001
0.010
0.001
0.033
0.003
0.014
0.975
0.681
0.730
0.963
0.973
0.004
0.088
0.143
0.051
0.963
0.123
Models were fitted by stepwise inclusion of fixed effects.
Likelihood ratio tests were applied to assess model improvement
(L ratio) and the statistical significance of the explanatory terms
(P values). Significant effects are marked in bold. The arrow indicates a decrease (fl) with increasing species richness. Note that
contrasts for functional group identity (factor with four levels)
and identity of each functional group separately (absent vs. present) were fitted in series of analyses replacing the term for species
identity.
The inclusion of a species’ proportional contribution to
community biomass (mean species proportion over time for
each species in each experimental community) fitted as
covariate before the experimental factors showed that temporal stability was larger in populations that contributed a larger
proportion to community biomass production (L = 317.24,
P < 0.001, Fig. 3a). Negative effects of increasing species
richness on population stability remained statistically significant after correcting for species biomass proportions, but
model improvement was small (L = 6.86, P = 0.009) compared to analysis without species biomass proportions as a
covariate (L = 49.19, P < 0.001, Table 1). Grouping of species occurrences according to their biomass proportions
(dominants, intermediates, subordinates) had significant
effects on population stability (L = 169.31, P = 0.001), but
these groups did not differ in their response to increasing species richness (L = 0.89, P = 0.641) or functional group number (L = 3.86, P = 0.145). Dominants represented 16% of
cases, intermediate 23% of cases and subordinates 61% of
cases across all species and communities, which were equally
distributed among species assigned to different functional
groups (v2 = 5.25, P = 0.512). Separate analyses of each
group showed that population stabilities of neither dominants
(L = 0.01, p = 0.973) nor intermediates (L = 0.27,
P = 0.602) or subordinates (L = 2.48, P = 0.115) depended
on species richness (Fig. 3b).
VARIANCE AND COVARIANCE RELATIONSHIPS
(HYPOTHESES 3 AND 4)
Summed variances across all species for a given plot decreased
from monocultures to mixtures but did not change in response
to increasing species richness of mixtures. Summed variances
were higher when legumes were present and lower when small
herbs were present compared to communities without these
functional groups (Table S1 in Supporting Information).
Summed covariances across all species did not change in
response to increasing species richness but decreased with
increasing functional group richness (Table S1).
The variance CV among all species decreased from monocultures to mixtures and with increasing species richness of
mixtures (Table S1, Fig. 4a). Functional group richness or the
presence of particular functional groups did not affect the variance CV. In contrast, the covariance CV increased slightly with
increasing species richness of mixtures, but functional group
richness fitted after species richness had decreasing effects on
covariance CV. The VR did not change in response to increasing species richness but decreased at increasing functional
group richness. Covariance CV < 0 and VR < 1 indicated
negatively correlated population dynamics among species.
The variance CV also decreased with increasing species richness at the between-functional-group level (Table S2 in Supporting Information, Fig. 4b) and was lower in communities
with legumes. The covariance CV and the VR at the betweenfunctional-group level varied independently of plant diversity
except for a higher covariance CV in communities with tall
herbs (Table S2). The significance of covariance CV < 0 and
of VR < 1 across all species-richness levels showed that temporal dynamics were negatively correlated between functional
groups.
The overall VR > 1 among species within legumes indicated correlated dynamics of legume species (Table S2,
Fig. 4c), whereas the VR close to 1 within grasses, small herbs
and tall herbs suggested that positive and negative covariances
among species cancelled each other out within these functional
groups (Table S2, Fig. 4d–f). The overall mean of the covariance CV among species within functional groups was not significantly different from zero. However, the covariance CV
within legumes increased with increasing species richness of
mixtures and resulted in an increasing VR (Table S2, Fig. 4c).
Similarly, the covariance CV increased with increasing species
richness of mixtures within small herbs, while the increase in
VR was only marginally significant (Table S2, Fig. 4e). The
covariance CV varied independently of the species richness of
mixtures within grasses and tall herbs. The VR tended to
increase with increasing species richness of mixtures within
grasses (Table S2, Fig. 4d). The VR within tall herbs decreased
with increasing functional group richness, was lower in communities with legumes and was higher in communities with
grasses than in communities without the respective functional
groups (Table S2). Increasing species richness of mixtures did
not affect the variance CV within any functional group, but
the variance CV within grasses and within tall herbs increased
at increasing functional group number. The variance CV
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469
Diversity–stability relationships 1465
Regression slopes of population stability SPop against species number
Fig. 2. Slopes of regressions of temporal
population stability SPop against species richness (±1 SE) for each species plotted against
mean values of population stability (±1 SE)
calculated across plots for (a) grasses, (b)
legumes, (c) small herbs and (d) tall herbs.
Species values above the broken line indicate
an increase in population stability with sown
species richness, and cases below the broken
line indicate a decrease in population stability with sown species richness. Black symbols
show species where the regression of population stability against species richness was significant (P < 0.05), while open symbols
show species where it was not.
1
0
0
–1
–1
–2
–2
–3
–3
–4
0.0
0.5
1.0
1.5
2.0
2.5
Small herbs
(c)
1
1
0
–1
–1
–2
–2
–3
–3
–4
0.0
0.5
1.0
1.5
2.0
2.5
Legumes
(b)
–4
0.0
0
0.5
1.0
2.0
2.5
2.0
2.5
Tall herbs
(d)
–4
0.0
1.5
0.5
1.0
1.5
Population stability SPop
(a)
Population stability SPop
2.5
Fig. 3. Population stability SPop (l ⁄ r) per
species plotted against a species’ proportional contribution to community biomass
as means (±1 SE) across all plots where a
particular species occurred (a) and population stability SPop (l ⁄ r) plotted against sown
species richness. Values in (b) are means
across all cases (±1 SE) per species-richness
level categorized according to their proportional contribution to community biomass
as dominants (>25%), intermediate
(5–25%) and subordinates (<5%).
Grasses
(a)
1
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0
10
20
30
40
50
60
0.0
Species biomass proportion:
(b)
> 25%
5-25%
< 5%
1
2
Biomass proportion (%)
within grasses was higher when legumes were present
(Table S2).
4
8
16
60
Species richness
The z value calculated across all species was z = 1.77. Values of z for individual species ranged from 1.45 to 2.19, but
only seven species reached z > 2.0.
COMMUNITY-WIDE SPECIES SYNCHRONY AND MEAN–
VARIANCE SCALING (HYPOTHESIS 4)
Discussion
Community-wide species synchrony (uP) was highly variable
at low species-richness levels. Overall, species synchrony
decreased with increasing species richness and was lower in
communities with small herbs (Table 2, Fig. 5a). Community
stability increased with sown species richness (Table 2,
Fig. 5b). In plant communities with a large stability in biomass
production, species synchrony was low, while communities
with lower stability varied largely in species synchrony
(Fig. 5c).
We studied population and community stability defined as
reciprocal of the coefficient of temporal variation, explored
mean–variance relationships and summed variances to test for
the portfolio effect and statistical averaging and used variance–covariance relationships to assess compensatory dynamics among species. The main results of these analyses show that
temporal stability at the population level decreases with
increasing species richness (hypothesis 1), that populations are
temporally stabilized dependent on their proportional
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469
1466 C. Roscher et al.
Variance CV
Variance ratio
Variance
2.0
1.5
1.0
1.0
0.5
0.5
0.0
0.0
2.0
Variance
2.0
1.5
–0.5
1
2
4
8
16
60
(c) within functional groups
between legumes
2.0
1.5
1.0
1.0
0.5
0.5
0.0
0.0
2.0
1
(e)
2
4
8
16
60
within functional groups
between small herbs
2.0
1.5
1.0
1.0
0.5
0.5
0.0
0.0
1
2
4
8
16
60
1
(d)
–0.5
1.5
–0.5
(b) between functional groups
–0.5
1.5
–0.5
Variance
across all species
(a)
Covariance CV
1
(f)
–0.5
1
Species richness
2
4
8
16
60
within functional groups
between grasses
2
4
8
16
60
within functional groups
between tall herbs
2
4
8
16
60
Species richness
Fig. 4. Within-plot variance CV, covariance
CV and variance ratio (VR) plotted against
species richness (a) across all species, (b)
between functional groups, (c) within
legumes, (d) within grasses, (e) within small
herbs and (f) within tall herbs. Values
(±1 SE) are means per species-richness level.
Cases above the dotted line for variance ratio
(VR > 1) indicate positively correlated
dynamics between populations or functional
groups; cases below the dotted line
(VR < 1) suggest negatively correlated
dynamics between species or functional
groups. For covariance CV cases that are
close to the broken line (CV = 0) suggest
that species or functional groups vary independently, while cases that deviate from the
broken line indicate that species or functional group dynamics are not independent
of each other.
Table 2. Summary of analyses of variance (anova) of species synchrony uP and community stability SCom (=l ⁄ r) based on peak above-ground
biomass of species from 2003 to 2009
Species synchrony uP
Block
Monoculture vs. Mixture
Mixture species richness
Functional group number
Legumes
Grasses
Small herbs
Tall herbs
Residuals
Community stability SCom
d.f.
MS
F
P
MS
F
P
3
1
1
1
1
1
1
1
75
0.046
5.566
1.465
0.083
<0.001
<0.001
0.098
0.079
0.024
1.94
235.00
61.84
3.52
0.01
0.01
4.31
3.45
0.131
<0.001fl
<0.001fl
0.064
0.936
0.936
0.041fl
0.067
0.680
13.506
17.254
0.103
0.199
0.002
0.416
1.268
0.674
1.01
20.05
25.61
0.15
0.29
<0.01
0.61
1.91
0.394
<0.001›
<0.001›
0.697
0.590
0.954
0.436
0.172
Given are the degrees of freedom (d.f.), mean sums of squares (MS), F ratios (F) and P values (P). Note that contrasts for the presence ⁄ absence of particular plant functional groups were fitted in series of analyses. Significant effects are marked in bold. Arrows indicate a significant decrease (fl) or increase (›) in analysed variables with increasing species richness, functional group number or presence
of particular plant functional groups.
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469
(a)
0.8
0.6
0.4
0.2
0.0
1
2
4
8
16
Species richness
60
7
(b)
6
5
4
3
2
1
0
1
2
4
8
16
60
Community stability SCom
Species synchrony
1.0
Community stability SCom
Diversity–stability relationships 1467
7
(c)
2 species
4 species
8 species
16 species
60 species
6
5
4
3
2
1
0
0.0
Species richness
0.2
0.4
0.6
0.8
1.0
Species synchrony
Fig. 5. Plot-specific community-wide species synchrony uP plotted against species richness (a), community stability SCom plotted against species
richness (b) and community-wide species synchrony uP plotted against community stability SCom (c).
contribution to community biomass production and almost
independently of plant species richness (hypothesis 2), that
uncorrelated dynamics are more likely among species assigned
to different functional groups than within functional groups
(hypothesis 3) and that plant species richness has little effects
on community stability when species dynamics are synchronous (hypothesis 4). These findings suggest that the diversity–
stability relationships, widely observed at the community level
across a range of taxonomic groups, are driven by abundance
distributions of individual species and by their less-synchronized dynamics with increasing richness.
POPULATION STABILITY
Although productivity at the community level increases with
species richness in the majority of biodiversity experiments,
including the ‘Jena Experiment’, effects of species richness on
biomass production of individual species are highly variable
(Hooper et al. 2005; Marquard et al. 2009a). However, owing
to the substitutive design often used in biodiversity experiments and the constant final yield law which also applies to
plant mixtures (He et al. 2005; Roscher et al. 2007; Weiner &
Freckleton 2010), it is likely that biomass proportions of
individual species populations decrease at increasing species
richness (hypothesis 1) if single species do not become highly
dominant. Therefore, the close correlation between species
proportional contribution to community biomass and temporal population stability SPop in a community (hypothesis 2)
may explain the mostly destabilizing effects of diversity on
populations (Fig. 2), which have been observed in a number of
biodiversity experiments (e.g. Tilman, Reich & Knops 2006;
van Ruijven & Berendse 2007; Hector et al. 2010). However,
our grouping of species occurrences into dominants (species
biomass contributes >25% to community biomass), intermediates (species biomass contributes between 5% and 25% to
community biomass) and subordinates (species biomass contributes <5% to community biomass) provided clear evidence
that species which contributed a larger proportion to community biomass had more stable populations (Fig. 3). A greater
stability in dominant than in subordinate species has been
observed in several empirical studies (Bai et al. 2004; Lepš
2004; Steiner et al. 2005; Polley, Wilsey & Derner 2007; Grman
et al. 2010). However, we also showed that population stabili-
ties of neither subordinates nor intermediates or dominants
changed in response to increasing species richness (Fig. 3b).
Therefore, overall negative effects of increasing species richness on population stabilities in our experiment are attributable to an increasing proportion of subordinate species in
more diverse communities. Thus, different patterns of species
diversity–abundance relationships are a possible explanation
for contrasting results. For instance, Valone & Hoffmann
(2003b) found positive effects of diversity on population stability, but population sizes increased with diversity in their study.
Similarly, Bai et al. (2004) observed positive relationships
between the stability of single species populations and plant
functional groups and their relative contribution to community biomass in natural grasslands.
The mean–variance scaling examined for each species separately, which mostly varied between 1 < z < 2 and only
rarely was z > 2, also supported the interpretation that temporal fluctuations of populations increased with increasing
species richness (hypothesis 1). The z value estimated across all
species was 1.77, which is consistent with other studies, where z
values in a range between 1 and 2 have been interpreted as
indicative of the portfolio effect (e.g. Tilman 1999; Steiner
et al. 2005; Polley, Wilsey & Derner 2007; van Ruijven &
Berendse 2007).
SPECIES SYNCHRONY
In addition to the portfolio effect or statistical averaging, i.e.
the manner in which temporal species variances scale with their
abundances, other mechanisms such as increasing negative
covariances in abundance of co-occurring species at higher
diversity (covariance effect) and overyielding effects have been
predicted to cause higher community stability and lower species stability with increasing species diversity (Tilman 1999).
Overyielding effects occur in most biodiversity experiments
with grassland species, including the ‘Jena Experiment’
(Roscher et al. 2005; Marquard et al. 2009b). In our study,
summed covariances and the covariance CV did not show a
dependence on species richness, but the overall mean of the
covariance CV < 0 and the variance ratio VR < 1 indicated
negatively correlated dynamics among species.
When variance–covariance relationships across species were
decomposed into the between-functional-group level and the
2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469
1468 C. Roscher et al.
species level within functional groups, a covariance CV < 0
and a VR < 1 was observed at the between-functional-group
level. In contrast (hypothesis 3), the VR was not significantly
different from 1 between species within functional groups or
even larger than 1 in the case of legumes (Fig. 4). At higher species richness (8, 16 and 60 plant species), we observed covariance CV > 0 and VR > 1 among species within the
functional groups of legumes, small herbs and grasses, suggesting positively correlated population dynamics of species within
these functional groups (Fig. 4c–e). Thus, it is likely that two
processes counteract at the within-functional-group level when
functionally similar species respond similarly to environmental
fluctuations or changes in resource availability (positive covariances) but compete more strongly for resources (negative
covariances) than species that are functionally less similar
(Hughes & Roughgarden 2000).
Although species synchrony on average decreased at
increasing species richness (hypothesis 4), it was highly variable
between different mixtures at lower species richness of two and
four species and less variable at the higher species-richness
levels (Fig. 5a). Obviously, low community-wide species
synchrony seemed to be a prerequisite for stability at the
community level, but temporal community stability was highly
variable at low levels of species synchrony (Fig. 5c). Thus, negative covariances attributable to either competitive interactions
between species or different responses to environmental fluctuations are responsible for community stability.
In summary, in accordance with a recent study by Isbell,
Polley & Wilsey (2009) in experimental grasslands, we found
that plant species richness increased community temporal stability through both a portfolio effect and a reduction in species
synchrony. However, our study emphasizes the prominent role
of species abundance distributions on the diversity–stability
relationship. We have clearly shown that dominant plant species tend to be more stabilized than subordinate species,
independent of community species richness. Thus, increasing
proportions of subordinate species at increasing species
richness are related to on average decreasing levels of
population stability, which are compensated through stabilizing effects of asynchronous species dynamics.
Acknowledgements
The ‘Jena Experiment’ is funded by the German Science Foundation (FOR
456) with additional support from the Max Planck Society and the University of Jena. We thank all the people, especially the gardeners, who helped
in maintaining the experiment and harvesting biomass. We thank two
anonymous reviewers for valuable comments on a previous version of the
manuscript.
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Received 21 March 2011; accepted 5 July 2011
Handling Editor: Jason Fridley
Supporting Information
Additional Supporting Information may be found in the online version of this article:
Table S1. Summary of analyses of variance (anova) of summed variances, summed covariances, variance CV, covariance CV and variance ratio (VR) based on peak above-ground biomass data across all
species.
Table S2. Summary of analyses of variance (anova) of variance CV,
covariance CV and variance ratio (VR) based on peak above-ground
biomass data between functional groups and species within each
functional group.
As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials may be reorganized for online delivery, but are not copy-edited or typeset.
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2011 The Authors. Journal of Ecology 2011 British Ecological Society, Journal of Ecology, 99, 1460–1469